Vertical Integration Analytics
Advanced Analytics applications, in particular Machine Learning, get more and more common in production environments, for instance in the form of Predictive Maintenance. However, so far the analyses are mostly focussed on single companies and their “data silos“. The common usage of data promises better results and consequently an increased efficiency. All participants of the business network, in particular the customers can benefit from this increased efficiency.
In the project Vertical Integration Analytics we investigate how this potential can be realized vertically along a value chain. Though, the sharing of production and meta data is critical due to data protection concerns. This issue can be addressed for example by the usage of abstracted or encrypted information which does not contain confidential information anymore. On this basis, it is assessed to what extent the efficiency gains can be realized, when Machine Learning is applied across company borders.
One use case for this is to leverage data regarding the production of machining tools to facilitate an optimal usage of this products by the customer. For instance, the customer receives recommendations how and how long concrete tools can be used.
01.06.2018 – 31.05.2021
Involved DSI Researchers
Treiss, A.; Walk, J.; Kühl, N.
2020. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020, 14-18 September 2020, Ghent, Belgium
Walk, J.; Kühl, N.; Schäfer, J.
2020. Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS-53), Grand Wailea, Maui, HI, January 7-10, 2020